Camel.memories.agent memories

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ChatHistoryMemory

class ChatHistoryMemory(AgentMemory):

• context_creator (BaseContextCreator): A model context creator.
• storage (BaseKeyValueStorage, optional): A storage backend for storing chat history. If None, an :obj:InMemoryKeyValueStorage will be used. (default: :obj:None)
• window_size (int, optional): The number of recent chat messages to retrieve. If not provided, the entire chat history will be retrieved. (default: :obj:None)
• agent_id (str, optional): The ID of the agent associated with the chat history.

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init

def __init__(
    self,
    context_creator: BaseContextCreator,
    storage: Optional[BaseKeyValueStorage] = None,
    window_size: Optional[int] = None,
    agent_id: Optional[str] = None
):

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agent_id

def agent_id(self):

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agent_id

def agent_id(self, val: Optional[str]):

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retrieve

def retrieve(self):

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write_records

def write_records(self, records: List[MemoryRecord]):

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get_context_creator

def get_context_creator(self):

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clear

def clear(self):

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clean_tool_calls

def clean_tool_calls(self):

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pop_records

def pop_records(self, count: int):

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remove_records_by_indices

def remove_records_by_indices(self, indices: List[int]):

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VectorDBMemory

class VectorDBMemory(AgentMemory):

• context_creator (BaseContextCreator): A model context creator.
• storage (BaseVectorStorage, optional): A vector storage storage. If None, an :obj:QdrantStorage will be used. (default: :obj:None)
• retrieve_limit (int, optional): The maximum number of messages to be added into the context. (default: :obj:3)
• agent_id (str, optional): The ID of the agent associated with the messages stored in the vector database.

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init

def __init__(
    self,
    context_creator: BaseContextCreator,
    storage: Optional[BaseVectorStorage] = None,
    retrieve_limit: int = 3,
    agent_id: Optional[str] = None
):

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agent_id

def agent_id(self):

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agent_id

def agent_id(self, val: Optional[str]):

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retrieve

def retrieve(self):

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write_records

def write_records(self, records: List[MemoryRecord]):

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get_context_creator

def get_context_creator(self):

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clear

def clear(self):

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pop_records

def pop_records(self, count: int):

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remove_records_by_indices

def remove_records_by_indices(self, indices: List[int]):

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LongtermAgentMemory

class LongtermAgentMemory(AgentMemory):

• context_creator (BaseContextCreator): A model context creator.
• chat_history_block (Optional[ChatHistoryBlock], optional): A chat history block. If None, a :obj:ChatHistoryBlock will be used. (default: :obj:None)
• vector_db_block (Optional[VectorDBBlock], optional): A vector database block. If None, a :obj:VectorDBBlock will be used. (default: :obj:None)
• retrieve_limit (int, optional): The maximum number of messages to be added into the context. (default: :obj:3)
• agent_id (str, optional): The ID of the agent associated with the chat history and the messages stored in the vector database.

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init

def __init__(
    self,
    context_creator: BaseContextCreator,
    chat_history_block: Optional[ChatHistoryBlock] = None,
    vector_db_block: Optional[VectorDBBlock] = None,
    retrieve_limit: int = 3,
    agent_id: Optional[str] = None
):

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agent_id

def agent_id(self):

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agent_id

def agent_id(self, val: Optional[str]):

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get_context_creator

def get_context_creator(self):

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retrieve

def retrieve(self):

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write_records

def write_records(self, records: List[MemoryRecord]):

• records (List[MemoryRecord]): Messages to be added to the vector database.

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clear

def clear(self):

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pop_records

def pop_records(self, count: int):

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remove_records_by_indices

def remove_records_by_indices(self, indices: List[int]):